This file contains regional models examining the functional relationship between agricultural landscape diversity and agricultural yields. This work applies hierachical Bayesian spatiotemporal modeling techniques to estimation of the effect of diversity of the agricultural landscape on the yield of corn, soy, and winter wheat.

REPRESENTING SPACE

The yield data available is at a county scale and the distribution of yields across spcae exhibits strong autocorrelation where yields in neighboring counties are more alike than yields in distant counties. This spatial autocorrelation is accounted for using a standard Conditional Autoreggressive dependency model based on adjacency for all counties in the conterminous US. In order to account for additional county-specific factors that contribute to yields a county iid random effect term is also included, yielding a Besag-York-Mollie (BYM) spatial dependency model. A seperate county adjacency matrix for each region-crop combination is created.

REGIONAL CORN MODELS

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
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Table: Summary Table of Model Estimates

                                                  mean             sd   0.025quant     0.5quant     0.975quant
----------------------------------------  ------------  -------------  -----------  -----------  -------------
(Intercept)                                     4.2142         0.0172       4.1804       4.2142         4.2479
PERC_IRR                                        0.0054         0.0007       0.0040       0.0054         0.0067
ACRES                                           0.0000         0.0000       0.0000       0.0000         0.0000
Precision for the Gaussian observations        46.6381         0.9349      44.7878      46.6476        48.4551
Precision for TP                               80.5151        38.9316      28.4105      72.8525       177.1899
Precision for SDD                              36.6466         9.6399      21.4419      35.4000        59.0074
Precision for GDD                             285.1326       123.7458     121.6174     259.3237       596.3728
Precision for SDI                          273449.3095   2909257.6177    2681.9743   35116.2912   1774927.0746
Precision for CNTY                             47.7249         3.2500      41.4817      47.6936        54.2345
Phi for CNTY                                    0.9968         0.0036       0.9874       0.9980         0.9999
Precision for AERCODE.id                      526.9856       184.7079     267.6953     492.4909       981.8349
Precision for AERCODE.id2                   10686.5281      3340.1745    5759.1103   10133.9753     18745.3029

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Table: Model Diagnostic Metrics

       DIC        CPO         MSE          R2
----------  ---------  ----------  ----------
 -5351.337   2603.654   0.0187517   0.7682262
Loading required package: viridis
Loading required package: viridisLite
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Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.7822243 0.0244935 0.7941849 2 
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]


Table: Summary Table of Model Estimates

                                                 mean           sd   0.025quant     0.5quant    0.975quant
----------------------------------------  -----------  -----------  -----------  -----------  ------------
(Intercept)                                    4.2234       0.0187       4.1872       4.2232        4.2605
PERC_IRR                                       0.0054       0.0007       0.0041       0.0054        0.0067
ACRES                                          0.0000       0.0000       0.0000       0.0000        0.0000
Precision for the Gaussian observations       46.6678       0.9335      44.8259      46.6742       48.4922
Precision for TP                              81.7406      38.7751      28.3291      74.6530      177.4969
Precision for SDD                             37.1012       9.5651      21.2329      36.1711       58.5881
Precision for GDD                            289.7569     117.9846     123.4765     268.6063      579.7156
Precision for SIDI                         24376.1951   55243.1038    1562.1032   10434.9888   133757.7083
Precision for CNTY                            47.6212       3.2674      41.7159      47.4261       54.5594
Phi for CNTY                                   0.9968       0.0035       0.9875       0.9979        0.9999
Precision for AERCODE.id                     527.7686     181.3298     265.3790     496.4499      969.5521
Precision for AERCODE.id2                  10978.4700    3306.4277    5889.8534   10511.4919    18771.4041

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Table: Model Diagnostic Metrics

       DIC    CPO         MSE          R2
----------  -----  ----------  ----------
 -5351.212   2604   0.0187377   0.7683414
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Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.7964911 0.0221084 0.7734521 2 
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]


Table: Summary Table of Model Estimates

                                                  mean             sd   0.025quant     0.5quant     0.975quant
----------------------------------------  ------------  -------------  -----------  -----------  -------------
(Intercept)                                     4.2187         0.0174       4.1846       4.2187         4.2529
PERC_IRR                                        0.0054         0.0007       0.0040       0.0054         0.0067
ACRES                                           0.0000         0.0000       0.0000       0.0000         0.0000
Precision for the Gaussian observations        47.6056         0.7046      46.7198      47.4576        49.3055
Precision for TP                               81.8447        38.7862      28.4100      74.7553       177.6123
Precision for SDD                              36.8643         9.6546      21.5116      35.6539        59.2073
Precision for GDD                             303.4041       119.1342     126.4317     285.5043       587.9036
Precision for RICH                         196116.5380   1340528.2409    3496.6615   36394.2116   1289586.3896
Precision for CNTY                             46.0678         0.7659      44.2480      46.2179        47.0624
Phi for CNTY                                    1.0000         0.0000       1.0000       1.0000         1.0000
Precision for AERCODE.id                      532.3527       177.8527     265.4198     505.2683       957.3958
Precision for AERCODE.id2                   10722.9675      3269.6746    5755.5650   10238.2459     18464.6578

[[2]]


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Table: Model Diagnostic Metrics

       DIC        CPO         MSE          R2
----------  ---------  ----------  ----------
 -5355.083   2605.403   0.0187545   0.7682742
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Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.671935 0.0278649 0.7550168 2 
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]


Table: Summary Table of Model Estimates

                                                mean          sd   0.025quant    0.5quant   0.975quant
----------------------------------------  ----------  ----------  -----------  ----------  -----------
(Intercept)                                   4.2862      0.0297       4.2276      4.2863       4.3444
PERC_IRR                                      0.0113      0.0012       0.0089      0.0113       0.0138
ACRES                                         0.0000      0.0000       0.0000      0.0000       0.0000
Precision for the Gaussian observations      19.8097      0.5188      18.8390     19.7903      20.8798
Precision for TP                            369.6189    275.6383      68.0318    299.4925    1092.1615
Precision for SDD                            23.1127      6.1267      12.7621     22.6054      36.6196
Precision for GDD                           127.0646     64.9522      43.4414    113.2748     291.9393
Precision for SDI                          1101.0559   1043.0219     190.8537    795.7093    3846.2163
Precision for CNTY                           14.5350      1.3744      12.0994     14.4355      17.5011
Phi for CNTY                                  0.9780      0.0158       0.9371      0.9817       0.9968
Precision for AERCODE.id                    138.8408     46.4999      69.5950    131.5660     250.1333
Precision for AERCODE.id2                  4722.7082   1538.8123    2368.6858   4504.2470    8358.7737

[[2]]


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Table: Model Diagnostic Metrics

       DIC         CPO        MSE         R2
----------  ----------  ---------  ---------
 -12.79171   -58.85196   0.043167   0.651193
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Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.6500659 0.0693833 0.6184632 2 
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]


Table: Summary Table of Model Estimates

                                                mean          sd   0.025quant    0.5quant   0.975quant
----------------------------------------  ----------  ----------  -----------  ----------  -----------
(Intercept)                                   4.2592      0.0313       4.1973      4.2593       4.3202
PERC_IRR                                      0.0116      0.0012       0.0092      0.0116       0.0140
ACRES                                         0.0000      0.0000       0.0000      0.0000       0.0000
Precision for the Gaussian observations      19.8277      0.5207      18.8518     19.8085      20.9008
Precision for TP                            362.4075    300.2303      69.5720    278.6169    1153.9014
Precision for SDD                            22.9556      6.3069      13.2752     22.0293      37.8919
Precision for GDD                           136.5472     72.0356      45.3191    120.8523     320.2600
Precision for SIDI                          890.2584    660.6546     184.5222    717.5824    2630.0188
Precision for CNTY                           15.0778      1.4788      12.4647     14.9714      18.2573
Phi for CNTY                                  0.9733      0.0183       0.9264      0.9775       0.9955
Precision for AERCODE.id                    132.2511     45.1442      67.9709    124.0851     242.3927
Precision for AERCODE.id2                  4281.3214   1489.8991    2200.5885   3999.0802    7964.2306

[[2]]


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Table: Model Diagnostic Metrics

       DIC         CPO         MSE          R2
----------  ----------  ----------  ----------
 -15.66584   -56.41943   0.0432211   0.6515934
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Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.6657347 0.0693209 0.5699762 2 
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]


Table: Summary Table of Model Estimates

                                                mean          sd   0.025quant    0.5quant   0.975quant
----------------------------------------  ----------  ----------  -----------  ----------  -----------
(Intercept)                                   4.3123      0.0295       4.2543      4.3124       4.3701
PERC_IRR                                      0.0111      0.0012       0.0087      0.0111       0.0135
ACRES                                         0.0000      0.0000       0.0000      0.0000       0.0000
Precision for the Gaussian observations      19.9199      0.5225      18.9004     19.9174      20.9579
Precision for TP                            374.3279    280.4680      71.5901    301.8961    1111.5748
Precision for SDD                            23.1228      6.3948      13.3762     22.1592      38.3178
Precision for GDD                           140.7063     73.4752      46.6680    124.9884     327.4825
Precision for RICH                          927.8370    547.1467     275.0302    798.5071    2337.5207
Precision for CNTY                           15.4579      1.5725      12.4538     15.4407      18.6223
Phi for CNTY                                  0.9655      0.0227       0.9087      0.9703       0.9943
Precision for AERCODE.id                    135.7763     44.4110      68.2123    129.3153     241.1614
Precision for AERCODE.id2                  4413.2071   1431.6697    2230.2598   4206.3938    7806.6522

[[2]]


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Table: Model Diagnostic Metrics

       DIC         CPO         MSE          R2
----------  ----------  ----------  ----------
 -26.53221   -51.76297   0.0429573   0.6521698
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Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.6625002 0.0577074 0.6652018 2 
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]


Table: Summary Table of Model Estimates

                                                  mean             sd   0.025quant     0.5quant    0.975quant
----------------------------------------  ------------  -------------  -----------  -----------  ------------
(Intercept)                                     4.5307         0.0241       4.4831       4.5308        4.5778
PERC_IRR                                        0.0304         0.0121       0.0069       0.0304        0.0544
ACRES                                           0.0000         0.0000       0.0000       0.0000        0.0000
Precision for the Gaussian observations        68.9226         4.2583      60.8439      68.8227       77.6088
Precision for TP                              480.3782       324.2380     112.5375     400.1823     1321.0250
Precision for SDD                             141.7108        60.8638      57.7704     130.2808      292.5995
Precision for GDD                          126729.4824   1548440.3412     877.6150   14109.9803   814105.9186
Precision for SDI                           63539.2527    657295.0844     637.1940    8330.8652   413642.8831
Precision for CNTY                            109.5745        26.8216      65.3538     106.8286      170.0906
Phi for CNTY                                    0.8090         0.1582       0.4009       0.8535        0.9874
Precision for AERCODE.id                    19905.3254     18273.5085    2219.1684   14760.7599    68630.2886
Precision for AERCODE.id2                   80652.0823     33059.0718   32244.3829   75473.8121   160148.8347

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Table: Model Diagnostic Metrics

       DIC        CPO         MSE          R2
----------  ---------  ----------  ----------
 -843.6714   409.5285   0.0121709   0.4902653
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Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.3740918 0.0186073 0.370263 2 
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]


Table: Summary Table of Model Estimates

                                                  mean             sd   0.025quant     0.5quant    0.975quant
----------------------------------------  ------------  -------------  -----------  -----------  ------------
(Intercept)                                     4.5349         0.0241       4.4877       4.5348        4.5825
PERC_IRR                                        0.0302         0.0121       0.0067       0.0302        0.0541
ACRES                                           0.0000         0.0000       0.0000       0.0000        0.0000
Precision for the Gaussian observations        68.8561         4.2492      60.8466      68.7367       77.5696
Precision for TP                              482.4479       323.4648     112.8373     403.2390     1319.0566
Precision for SDD                             142.1618        60.9802      58.3107     130.6291      293.4168
Precision for GDD                          124352.5624   1415065.5894     892.0100   14568.9930   807937.8665
Precision for SIDI                         154533.7301   2126483.7119     614.5955   14608.8256   992241.6155
Precision for CNTY                            108.6734        26.5511      65.7622     105.5538      169.4989
Phi for CNTY                                    0.8085         0.1569       0.4039       0.8525        0.9860
Precision for AERCODE.id                    19539.1995     18422.0983    2233.5206   14294.4051    68839.0652
Precision for AERCODE.id2                   80784.9203     33418.2548   32520.2030   75304.7852   161657.5236

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Table: Model Diagnostic Metrics

       DIC        CPO         MSE          R2
----------  ---------  ----------  ----------
 -843.4376   409.2088   0.0121998   0.4900502
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Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.6085819 0.0150998 0.4229685 2 
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
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Table: Summary Table of Model Estimates

                                                 mean            sd   0.025quant     0.5quant    0.975quant
----------------------------------------  -----------  ------------  -----------  -----------  ------------
(Intercept)                                    4.5331        0.0232       4.4874       4.5332        4.5785
PERC_IRR                                       0.0301        0.0120       0.0066       0.0301        0.0540
ACRES                                          0.0000        0.0000       0.0000       0.0000        0.0000
Precision for the Gaussian observations       68.8190        4.2516      60.7831      68.7074       77.5203
Precision for TP                             479.6983      325.3207     112.4885     398.7137     1326.2547
Precision for SDD                            142.6346       61.3090      58.6101     130.9584      294.7995
Precision for GDD                          50341.5955   271675.7196     610.6359   10366.0334   337266.0891
Precision for RICH                         49117.9100   210139.9035     689.8862   12047.3893   324105.6568
Precision for CNTY                           109.3419       26.6873      65.2932     106.6325      169.4767
Phi for CNTY                                   0.8110        0.1566       0.4067       0.8549        0.9876
Precision for AERCODE.id                   19711.0277    18164.5883    2181.6094   14588.0545    68098.9763
Precision for AERCODE.id2                  81135.1988    33809.4586   32954.8503   75348.8264   163123.5745

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Table: Model Diagnostic Metrics

       DIC        CPO         MSE          R2
----------  ---------  ----------  ----------
 -843.4964   409.3231   0.0121984   0.4896312
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Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.3874911 0.0211235 0.4451655 2 
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
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Table: Summary Table of Model Estimates

                                                 mean           sd   0.025quant     0.5quant    0.975quant
----------------------------------------  -----------  -----------  -----------  -----------  ------------
(Intercept)                                    4.7234       0.0652       4.5986       4.7222        4.8546
PERC_IRR                                       0.0144       0.0030       0.0084       0.0145        0.0202
ACRES                                          0.0000       0.0000       0.0000       0.0000        0.0000
Precision for the Gaussian observations       42.7950       4.5498      34.3628      42.6394       52.1872
Precision for TP                             636.6272    1053.9992      45.0376     333.3069     3113.3750
Precision for SDD                            189.5203     142.0761      42.0396     151.3506      561.8880
Precision for GDD                           1170.5365    2482.2251      72.5194     521.2839     6310.6199
Precision for SDI                           2013.2868    7623.2833      68.8215     589.6933    12603.5785
Precision for CNTY                            11.1257       2.3513       7.1539      10.9106       16.3661
Phi for CNTY                                   0.4917       0.1668       0.1760       0.4936        0.8023
Precision for AERCODE.id                   19110.2563   18812.8186    1484.0891   13621.2283    68789.4449
Precision for AERCODE.id2                  45730.3058   25497.2403   13450.1910   40267.5243   110535.6248

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Table: Model Diagnostic Metrics

      DIC        CPO         MSE          R2
---------  ---------  ----------  ----------
 -166.502   61.97449   0.0162994   0.8047957
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Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.7795008 0.0346998 0.8047987 2 
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]


Table: Summary Table of Model Estimates

                                                 mean           sd   0.025quant     0.5quant    0.975quant
----------------------------------------  -----------  -----------  -----------  -----------  ------------
(Intercept)                                    4.7145       0.0587       4.6008       4.7138        4.8316
PERC_IRR                                       0.0128       0.0032       0.0065       0.0129        0.0190
ACRES                                          0.0000       0.0000       0.0000       0.0000        0.0000
Precision for the Gaussian observations       41.7821       4.3646      33.7718      41.5883       50.9417
Precision for TP                             618.8255     948.7052      44.5808     339.8249     2926.9039
Precision for SDD                            232.1437     192.7070      47.0864     177.7403      743.2756
Precision for GDD                           3227.6410   13591.1091     103.5062     875.7718    20380.8610
Precision for SIDI                           412.7495     592.1571      32.4183     236.6736     1879.9395
Precision for CNTY                            11.6027       2.4388       7.5016      11.3704       17.0634
Phi for CNTY                                   0.5459       0.1670       0.2173       0.5517        0.8441
Precision for AERCODE.id                   20725.8742   19798.0209    1805.3359   15042.2780    73456.7466
Precision for AERCODE.id2                  48767.9135   26218.2604   14897.2504   43343.7629   115008.5543

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Table: Model Diagnostic Metrics

       DIC        CPO         MSE          R2
----------  ---------  ----------  ----------
 -165.2358   63.11838   0.0167083   0.8032014
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Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.8485234 0.0357488 0.7818907 2 
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]


Table: Summary Table of Model Estimates

                                                 mean           sd   0.025quant     0.5quant    0.975quant
----------------------------------------  -----------  -----------  -----------  -----------  ------------
(Intercept)                                    4.7872       0.0764       4.6412       4.7863        4.9388
PERC_IRR                                       0.0116       0.0034       0.0050       0.0116        0.0181
ACRES                                          0.0000       0.0000       0.0000       0.0000        0.0000
Precision for the Gaussian observations       42.2996       4.4303      34.1639      42.1051       51.5914
Precision for TP                             453.7813     568.9475      42.0697     282.9200     1918.9353
Precision for SDD                            216.4446     170.2710      45.3111     169.6801      665.1345
Precision for GDD                           3892.7117   19197.2501     110.3369     941.9450    25002.0731
Precision for RICH                           371.9123     437.9684      52.3493     240.5930     1489.4494
Precision for CNTY                            11.6800       2.4662       7.5442      11.4406       17.2126
Phi for CNTY                                   0.5628       0.1631       0.2329       0.5728        0.8464
Precision for AERCODE.id                   24785.4514   26818.3102    2114.6817   16791.7251    95817.5010
Precision for AERCODE.id2                  48029.9805   25485.6506   14320.2083   43013.2600   111582.1278

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Table: Model Diagnostic Metrics

       DIC        CPO         MSE          R2
----------  ---------  ----------  ----------
 -165.6465   62.95575   0.0163854   0.8038771
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Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.9053707 0.0570671 0.5103345 2

REGIONAL SOY MODELS

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
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Table: Summary Table of Model Estimates

                                                mean          sd   0.025quant    0.5quant   0.975quant
----------------------------------------  ----------  ----------  -----------  ----------  -----------
(Intercept)                                   3.3511      0.0152       3.3213      3.3511       3.3808
PERC_IRR                                      0.0049      0.0006       0.0037      0.0049       0.0060
ACRES                                         0.0000      0.0000       0.0000      0.0000       0.0000
Precision for the Gaussian observations      77.0549      1.6057      73.9227     77.0461      80.2463
Precision for TP                            489.4658    431.3383      93.6094    365.2248    1626.4020
Precision for SDD                            87.0945     25.2817      48.0979     83.5560     146.5130
Precision for GDD                           246.3878    100.2429     103.3409    229.1061     490.6404
Precision for SDI                          1836.2670   1037.8458     530.9151   1611.2174    4458.2925
Precision for CNTY                           60.7216      4.2216      52.9030     60.5587      69.4731
Phi for CNTY                                  0.9972      0.0035       0.9875      0.9984       1.0000
Precision for AERCODE.id                    268.7808     76.2214     146.9085    259.7928     443.3791
Precision for AERCODE.id2                  6488.5355   1788.6567    3636.5519   6269.3960   10624.1607

[[2]]


[[3]]


Table: Model Diagnostic Metrics

       DIC        CPO         MSE          R2
----------  ---------  ----------  ----------
 -7881.263   3828.274   0.0112339   0.8024213
[[1]]


[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.773622 0.0156994 0.7815069 2 
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]


Table: Summary Table of Model Estimates

                                                  mean             sd   0.025quant     0.5quant     0.975quant
----------------------------------------  ------------  -------------  -----------  -----------  -------------
(Intercept)                                     3.3516         0.0154       3.3215       3.3516         3.3818
PERC_IRR                                        0.0052         0.0006       0.0040       0.0052         0.0063
ACRES                                           0.0000         0.0000       0.0000       0.0000         0.0000
Precision for the Gaussian observations        76.5530         1.6393      73.5521      76.4689        79.9804
Precision for TP                              489.6890       517.0285      92.2873     334.0849      1826.3002
Precision for SDD                              83.9492        25.1102      46.4091      79.9470       144.1970
Precision for GDD                             240.3204        96.0015      90.9586     228.8686       460.6279
Precision for SIDI                         331193.3723   2502330.3488    4171.6169   54916.9534   2190790.4132
Precision for CNTY                             58.9527         4.4171      51.5269      58.4881        68.7498
Phi for CNTY                                    0.9980         0.0026       0.9910       0.9989         1.0000
Precision for AERCODE.id                      267.8147        77.2990     134.8962     262.8321       434.2977
Precision for AERCODE.id2                    6012.3177      1797.5836    3324.6093    5725.8407     10323.8887

[[2]]


[[3]]


Table: Model Diagnostic Metrics

       DIC        CPO         MSE          R2
----------  ---------  ----------  ----------
 -7844.815   3811.576   0.0113053   0.8014569
[[1]]


[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.7076951 0.0198243 0.7686717 2 
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]


Table: Summary Table of Model Estimates

                                                  mean            sd   0.025quant     0.5quant    0.975quant
----------------------------------------  ------------  ------------  -----------  -----------  ------------
(Intercept)                                     3.3479        0.0153       3.3177       3.3479        3.3779
PERC_IRR                                        0.0052        0.0006       0.0040       0.0052        0.0063
ACRES                                           0.0000        0.0000       0.0000       0.0000        0.0000
Precision for the Gaussian observations        76.6007        1.6017      73.4316      76.6177       79.7077
Precision for TP                              482.0734      444.6789      93.7467     351.4962     1651.4410
Precision for SDD                              84.7098       24.1640      46.1749      81.8042      140.2104
Precision for GDD                             234.1006       94.2184      98.8615     218.1275      462.6958
Precision for RICH                         109800.8474   640800.4146    2730.2873   23507.2965   714435.0448
Precision for CNTY                             59.2449        4.0557      51.5475      59.1559       67.4991
Phi for CNTY                                    0.9974        0.0033       0.9885       0.9986        1.0000
Precision for AERCODE.id                      262.0719       73.2632     143.3197     254.0177      428.7565
Precision for AERCODE.id2                    6335.1449     1780.2342    3566.2203    6092.6510    10497.6984

[[2]]


[[3]]


Table: Model Diagnostic Metrics

       DIC        CPO         MSE          R2
----------  ---------  ----------  ----------
 -7845.297   3811.898   0.0113026   0.8011916
[[1]]


[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.764215 0.0166556 0.7871187 2 
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]


Table: Summary Table of Model Estimates

                                                mean          sd   0.025quant    0.5quant   0.975quant
----------------------------------------  ----------  ----------  -----------  ----------  -----------
(Intercept)                                   3.2516      0.0201       3.2122      3.2515       3.2912
PERC_IRR                                      0.0079      0.0011       0.0057      0.0079       0.0102
ACRES                                         0.0000      0.0000       0.0000      0.0000       0.0000
Precision for the Gaussian observations      32.5486      0.9033      30.8377     32.5228      34.3905
Precision for TP                            659.1634    504.6714     137.6350    523.2940    1986.7935
Precision for SDD                            83.4114     24.8616      44.9844     79.9559     141.9145
Precision for GDD                           389.5829    214.3140     119.6602    342.9701     935.4386
Precision for SDI                          2398.0794   1993.0794     492.4664   1833.8770    7643.7093
Precision for CNTY                           15.4742      1.6927      12.3017     15.4344      18.9438
Phi for CNTY                                  0.9671      0.0173       0.9252      0.9701       0.9914
Precision for AERCODE.id                    202.7514     76.0025      93.1505    189.8009     387.3861
Precision for AERCODE.id2                  8556.8143   3149.9192    3999.7800   8023.8770   16189.9118

[[2]]


[[3]]


Table: Model Diagnostic Metrics

       DIC        CPO         MSE          R2
----------  ---------  ----------  ----------
 -1773.304   783.8237   0.0263707   0.6541006
[[1]]


[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.6682249 0.0409116 0.5814491 2 
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]


Table: Summary Table of Model Estimates

                                                mean          sd   0.025quant    0.5quant   0.975quant
----------------------------------------  ----------  ----------  -----------  ----------  -----------
(Intercept)                                   3.2241      0.0213       3.1820      3.2242       3.2657
PERC_IRR                                      0.0084      0.0011       0.0062      0.0084       0.0107
ACRES                                         0.0000      0.0000       0.0000      0.0000       0.0000
Precision for the Gaussian observations      32.5439      0.8966      30.8154     32.5322      34.3430
Precision for TP                            650.4807    497.5825     132.2043    517.5282    1963.8826
Precision for SDD                            83.0788     24.9668      44.9360     79.4695     142.0091
Precision for GDD                           362.3420    200.3892     113.9314    317.3217     875.8177
Precision for SIDI                         1954.6394   1774.8975     337.3591   1443.3084    6648.5265
Precision for CNTY                           15.7081      1.7325      12.4621     15.6663      19.2628
Phi for CNTY                                  0.9675      0.0173       0.9261      0.9704       0.9918
Precision for AERCODE.id                    178.7108     63.7643      83.6578    168.9331     331.2374
Precision for AERCODE.id2                  7567.9576   2730.2159    3605.0875   7109.6133   14165.2974

[[2]]


[[3]]


Table: Model Diagnostic Metrics

       DIC        CPO         MSE          R2
----------  ---------  ----------  ----------
 -1771.599   785.0175   0.0264286   0.6539069
[[1]]


[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.5844853 0.0462006 0.5702695 2 
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]


Table: Summary Table of Model Estimates

                                                mean          sd   0.025quant    0.5quant   0.975quant
----------------------------------------  ----------  ----------  -----------  ----------  -----------
(Intercept)                                   3.2648      0.0201       3.2255      3.2648       3.3045
PERC_IRR                                      0.0083      0.0011       0.0061      0.0083       0.0105
ACRES                                         0.0000      0.0000       0.0000      0.0000       0.0000
Precision for the Gaussian observations      32.7946      0.9087      31.0074     32.7989      34.5728
Precision for TP                            639.7733    510.5112     134.2234    498.1367    1993.3586
Precision for SDD                            82.9976     24.4424      42.9061     80.5758     138.0170
Precision for GDD                           380.8714    206.7266     114.3952    337.8512     901.2940
Precision for RICH                         1112.4344    736.9620     316.9518    917.8801    3051.3268
Precision for CNTY                           15.4798      1.7073      12.4387     15.3656      19.1424
Phi for CNTY                                  0.9654      0.0179       0.9219      0.9686       0.9901
Precision for AERCODE.id                    184.7192     66.3022      84.5485    175.1294     341.2241
Precision for AERCODE.id2                  8131.1315   2873.1375    3763.5885   7724.0613   14895.5227

[[2]]


[[3]]


Table: Model Diagnostic Metrics

       DIC        CPO         MSE          R2
----------  ---------  ----------  ----------
 -1785.302   789.4312   0.0261885   0.6555727
[[1]]


[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.6708578 0.0349296 0.6770893 2 
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]


Table: Summary Table of Model Estimates

                                                 mean           sd   0.025quant     0.5quant    0.975quant
----------------------------------------  -----------  -----------  -----------  -----------  ------------
(Intercept)                                    3.5601       0.0288       3.5037       3.5600        3.6170
PERC_IRR                                       0.0062       0.0086      -0.0106       0.0062        0.0231
ACRES                                          0.0000       0.0000       0.0000       0.0000        0.0000
Precision for the Gaussian observations       73.5285       5.3409      63.4523      73.3865       84.4692
Precision for TP                             648.3918     713.3188      90.5557     435.4605     2496.7883
Precision for SDD                            123.1463      61.4829      43.4198     110.2142      278.6273
Precision for GDD                           1063.4320    1062.8725     151.1732     751.6247     3842.9753
Precision for SDI                          19330.6953   93539.0951     469.6689    4646.4268   125157.1814
Precision for CNTY                           100.8677      28.7769      55.7033      97.0821      167.9791
Phi for CNTY                                   0.7518       0.1996       0.2612       0.8067        0.9849
Precision for AERCODE.id                   13579.6362   25735.5934    1008.9124    6547.9241    69710.2825
Precision for AERCODE.id2                  57806.1127   28766.8662   19981.6283   51936.6923   129952.6756

[[2]]


[[3]]


Table: Model Diagnostic Metrics

       DIC        CPO         MSE          R2
----------  ---------  ----------  ----------
 -668.8126   318.0198   0.0111219   0.4941469
[[1]]


[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.6756589 0.0185459 0.2089638 2 
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]


Table: Summary Table of Model Estimates

                                                 mean           sd   0.025quant     0.5quant    0.975quant
----------------------------------------  -----------  -----------  -----------  -----------  ------------
(Intercept)                                    3.5421       0.0318       3.4787       3.5425        3.6035
PERC_IRR                                       0.0063       0.0083      -0.0100       0.0063        0.0227
ACRES                                          0.0000       0.0000       0.0000       0.0000        0.0000
Precision for the Gaussian observations       74.5307       5.4902      64.2467      74.3555       85.8560
Precision for TP                             633.1501     699.9104      88.9814     424.0805     2451.3873
Precision for SDD                            117.7142      58.8526      42.4181     105.0265      266.4400
Precision for GDD                           1454.7276    1729.4192     175.2000     937.0108     5879.5005
Precision for SIDI                          5969.4887   27514.9356     109.4764    1435.0037    38828.9138
Precision for CNTY                            94.2517      25.3091      53.4767      91.3268      152.3021
Phi for CNTY                                   0.7803       0.1830       0.3128       0.8334        0.9864
Precision for AERCODE.id                   12769.1891   24553.9536     928.8708    6097.3785    66323.2036
Precision for AERCODE.id2                  59328.2963   29482.4920   20431.7906   53358.4518   132943.9857

[[2]]


[[3]]


Table: Model Diagnostic Metrics

       DIC        CPO         MSE          R2
----------  ---------  ----------  ----------
 -674.7631   320.3977   0.0108975   0.4996233
[[1]]


[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.431855 0.017372 0.4336319 2 
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]


Table: Summary Table of Model Estimates

                                                 mean           sd   0.025quant     0.5quant    0.975quant
----------------------------------------  -----------  -----------  -----------  -----------  ------------
(Intercept)                                    3.5491       0.0289       3.4924       3.5491        3.6058
PERC_IRR                                       0.0057       0.0086      -0.0111       0.0057        0.0226
ACRES                                          0.0000       0.0000       0.0000       0.0000        0.0000
Precision for the Gaussian observations       77.7987       5.8658      66.8247      77.6065       89.9127
Precision for TP                             638.0665     683.1682      89.0978     434.8378     2417.8846
Precision for SDD                            109.1177      52.9034      40.3406      98.0047      242.1444
Precision for GDD                           1450.3749    1710.1968     179.5254     938.4626     5821.1577
Precision for RICH                           981.4053    1084.0829     165.8733     655.5880     3771.2430
Precision for CNTY                            82.6632      21.2746      49.1055      79.8674      131.9407
Phi for CNTY                                   0.8166       0.1534       0.4130       0.8616        0.9859
Precision for AERCODE.id                   11024.2823   20004.6415     734.7119    5438.3841    55754.1302
Precision for AERCODE.id2                  60049.4567   29913.6688   20396.0700   54076.1592   134729.3246

[[2]]


[[3]]


Table: Model Diagnostic Metrics

       DIC        CPO         MSE          R2
----------  ---------  ----------  ----------
 -686.0494   325.6193   0.0103897   0.5109601
[[1]]


[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.3576449 0.0201557 0.3276189 2

REGIONAL WINTER WHEAT MODELS

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]


Table: Summary Table of Model Estimates

                                                mean          sd   0.025quant    0.5quant   0.975quant
----------------------------------------  ----------  ----------  -----------  ----------  -----------
(Intercept)                                   3.9170      0.0177       3.8823      3.9170       3.9516
PERC_IRR                                      0.0044      0.0007       0.0030      0.0044       0.0058
ACRES                                         0.0000      0.0000       0.0000      0.0000       0.0000
Precision for the Gaussian observations      40.6061      1.0829      38.4958     40.5999      42.7606
Precision for TP                            177.4851    108.3737      50.8788    151.2346     457.3401
Precision for SDD                           307.4415    148.2713     111.0211    277.4644     680.3222
Precision for GDD                           881.7632    566.2082     243.2962    739.0115    2362.1181
Precision for SDI                          3111.4131   3938.9488     397.1376   1933.9093   13049.3001
Precision for CNTY                           35.9773      3.5055      29.7111     35.7479      43.4940
Phi for CNTY                                  0.9958      0.0046       0.9834      0.9972       0.9998
Precision for AERCODE.id                    157.6080     50.4619      79.4532    150.8640     275.2732
Precision for AERCODE.id2                  4462.6703   1363.5846    2352.8179   4274.6807    7670.9137

[[2]]


[[3]]


Table: Model Diagnostic Metrics

       DIC        CPO         MSE          R2
----------  ---------  ----------  ----------
 -2614.601   1270.864   0.0213955   0.7052124
[[1]]


[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.6827445 0.0321062 0.7070313 2 
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]


Table: Summary Table of Model Estimates

                                                 mean            sd   0.025quant     0.5quant    0.975quant
----------------------------------------  -----------  ------------  -----------  -----------  ------------
(Intercept)                                    3.9283        0.0174       3.8946       3.9281        3.9631
PERC_IRR                                       0.0045        0.0007       0.0031       0.0045        0.0059
ACRES                                          0.0000        0.0000       0.0000       0.0000        0.0000
Precision for the Gaussian observations       40.4519        1.0860      38.4117      40.4136       42.6858
Precision for TP                             172.6210      106.3376      50.1747     146.3412      449.8994
Precision for SDD                            317.8087      154.9366     113.5751     286.2056      708.1135
Precision for GDD                            870.9885      570.3632     243.1515     723.2363     2367.3339
Precision for SIDI                         79680.5972   436017.1906    1417.3301   17064.5860   524546.2194
Precision for CNTY                            36.0509        3.7394      29.8057      35.6376       44.4402
Phi for CNTY                                   0.9954        0.0047       0.9830       0.9968        0.9998
Precision for AERCODE.id                     150.9631       53.3388      76.3047     140.9454      282.4260
Precision for AERCODE.id2                   4595.9493     1361.9321    2387.6437    4450.9149     7694.9411

[[2]]


[[3]]


Table: Model Diagnostic Metrics

      DIC        CPO         MSE          R2
---------  ---------  ----------  ----------
 -2605.13   1266.922   0.0214951   0.7047437
[[1]]


[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.7140845 0.030918 0.6611112 2 
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]


Table: Summary Table of Model Estimates

                                                mean          sd   0.025quant    0.5quant   0.975quant
----------------------------------------  ----------  ----------  -----------  ----------  -----------
(Intercept)                                   3.9275      0.0176       3.8930      3.9274       3.9622
PERC_IRR                                      0.0044      0.0007       0.0030      0.0044       0.0058
ACRES                                         0.0000      0.0000       0.0000      0.0000       0.0000
Precision for the Gaussian observations      40.4612      1.0790      38.4050     40.4376      42.6382
Precision for TP                            177.0722    107.2298      50.6860    151.3779     454.2673
Precision for SDD                           323.1476    159.4549     115.0831    289.9894     726.1629
Precision for GDD                           883.3958    578.6991     246.1655    733.5533    2401.6732
Precision for RICH                         4201.9034   4796.0082     579.2553   2766.5147   16600.2093
Precision for CNTY                           36.7769      3.5876      30.0675     36.6803      44.1188
Phi for CNTY                                  0.9957      0.0047       0.9830      0.9971       0.9998
Precision for AERCODE.id                    152.9836     51.5971      77.9096    144.1892     278.3894
Precision for AERCODE.id2                  4488.5038   1349.8544    2373.6571   4312.8271    7633.8546

[[2]]


[[3]]


Table: Model Diagnostic Metrics

       DIC        CPO         MSE          R2
----------  ---------  ----------  ----------
 -2611.032   1270.057   0.0214735   0.7046792
[[1]]


[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.6750961 0.031759 0.6688041 2 
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]


Table: Summary Table of Model Estimates

                                                mean          sd   0.025quant    0.5quant   0.975quant
----------------------------------------  ----------  ----------  -----------  ----------  -----------
(Intercept)                                   3.4149      0.0223       3.3711      3.4149       3.4586
PERC_IRR                                      0.0082      0.0011       0.0060      0.0082       0.0105
ACRES                                         0.0000      0.0000       0.0000      0.0000       0.0000
Precision for the Gaussian observations      23.9822      0.7083      22.6105     23.9749      25.3996
Precision for TP                             87.3874     38.8511      33.2437     80.4188     182.4302
Precision for SDD                            64.5991     18.8261      34.7822     62.2622     108.0890
Precision for GDD                           302.1134    218.8343      82.4897    240.9459     881.3839
Precision for SDI                          2514.0350   3284.5731     245.5074   1531.7012   10803.2916
Precision for CNTY                           28.7350      3.5386      22.4686     28.4964      36.3426
Phi for CNTY                                  0.9576      0.0410       0.8448      0.9700       0.9963
Precision for AERCODE.id                     28.1938      7.5541      15.9889     27.3316      45.4541
Precision for AERCODE.id2                  1233.4299    335.2599     682.7868   1199.1327    1991.4658

[[2]]


[[3]]


Table: Model Diagnostic Metrics

       DIC        CPO         MSE          R2
----------  ---------  ----------  ----------
 -608.3738   259.1074   0.0358235   0.7610683
[[1]]


[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.8656774 0.0490918 0.7415549 2 
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]


Table: Summary Table of Model Estimates

                                                mean          sd   0.025quant    0.5quant   0.975quant
----------------------------------------  ----------  ----------  -----------  ----------  -----------
(Intercept)                                   3.4137      0.0247       3.3645      3.4139       3.4617
PERC_IRR                                      0.0083      0.0012       0.0061      0.0083       0.0106
ACRES                                         0.0000      0.0000       0.0000      0.0000       0.0000
Precision for the Gaussian observations      23.9945      0.7084      22.6143     23.9906      25.4034
Precision for TP                             88.7569     39.3769      33.4129     81.8420     185.0396
Precision for SDD                            63.8100     18.4931      34.2097     61.6589     106.1652
Precision for GDD                           312.4643    220.5891      85.1093    252.0573     896.4737
Precision for SIDI                         2347.6035   3764.1767     224.3390   1266.2632   11154.7376
Precision for CNTY                           28.1254      3.4472      21.5035     28.1152      35.0049
Phi for CNTY                                  0.9725      0.0268       0.9008      0.9808       0.9988
Precision for AERCODE.id                     27.1506      7.3095      15.3806     26.2982      43.9157
Precision for AERCODE.id2                  1164.3404    340.5801     650.9135   1111.4089    1978.4178

[[2]]


[[3]]


Table: Model Diagnostic Metrics

      DIC        CPO         MSE          R2
---------  ---------  ----------  ----------
 -611.411   261.3331   0.0357279   0.7613734
[[1]]


[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.7867415 0.0496004 0.7294256 2 
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]


Table: Summary Table of Model Estimates

                                                mean          sd   0.025quant    0.5quant   0.975quant
----------------------------------------  ----------  ----------  -----------  ----------  -----------
(Intercept)                                   3.4074      0.0230       3.3620      3.4075       3.4522
PERC_IRR                                      0.0082      0.0011       0.0060      0.0082       0.0104
ACRES                                         0.0000      0.0000       0.0000      0.0000       0.0000
Precision for the Gaussian observations      24.0298      0.7138      22.6241     24.0350      25.4228
Precision for TP                             87.7558     39.0042      33.1217     80.8458     183.1775
Precision for SDD                            64.1986     18.6833      34.7667     61.8016     107.6672
Precision for GDD                           320.2854    224.8064      86.1243    259.3603     914.0443
Precision for RICH                         3929.0164   5149.5697     492.4506   2394.0703   16784.3256
Precision for CNTY                           28.1838      3.4787      21.5202     28.1659      35.1429
Phi for CNTY                                  0.9708      0.0283       0.8958      0.9796       0.9988
Precision for AERCODE.id                     27.2509      7.4958      15.5522     26.2418      44.7396
Precision for AERCODE.id2                  1214.9231    331.0162     679.6608   1177.9879    1966.8760

[[2]]


[[3]]


Table: Model Diagnostic Metrics

      DIC        CPO         MSE          R2
---------  ---------  ----------  ----------
 -611.938   261.6575   0.0356628   0.7614084
[[1]]


[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.714093 0.0552462 0.7321951 2 
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]


Table: Summary Table of Model Estimates

                                                 mean            sd   0.025quant     0.5quant    0.975quant
----------------------------------------  -----------  ------------  -----------  -----------  ------------
(Intercept)                                    3.8989        0.0279       3.8430       3.8993        3.9526
PERC_IRR                                       0.0131        0.0099      -0.0062       0.0131        0.0327
ACRES                                          0.0000        0.0000       0.0000       0.0000        0.0000
Precision for the Gaussian observations       85.9081        7.5420      71.7963      85.6756      101.4425
Precision for TP                           34917.3850   152883.7602     814.9333    8931.6461   225123.3940
Precision for SDD                           2029.2295     3001.5359     206.3372    1149.7798     9287.6783
Precision for GDD                            291.5210      147.5742     100.4794     260.4684      665.4212
Precision for SDI                           2090.7468     2991.0976     180.6981    1204.4375     9483.1228
Precision for CNTY                            90.7888       22.5324      54.3388      88.1639      142.4760
Phi for CNTY                                   0.3520        0.1963       0.0619       0.3224        0.7805
Precision for AERCODE.id                    7415.8052    16391.5010     419.2259    3196.0686    40768.4247
Precision for AERCODE.id2                  26727.8727    15519.3666    8084.9294   23091.0691    66719.7064

[[2]]


[[3]]


Table: Model Diagnostic Metrics

       DIC       CPO         MSE          R2
----------  --------  ----------  ----------
 -533.6433   257.783   0.0091701   0.6212439
[[1]]


[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.4406014 0.0211711 0.5252146 2 
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]


Table: Summary Table of Model Estimates

                                                 mean            sd   0.025quant     0.5quant    0.975quant
----------------------------------------  -----------  ------------  -----------  -----------  ------------
(Intercept)                                    3.9227        0.0252       3.8741       3.9225        3.9731
PERC_IRR                                       0.0104        0.0093      -0.0080       0.0104        0.0287
ACRES                                          0.0000        0.0000       0.0000       0.0000        0.0000
Precision for the Gaussian observations       84.9419        7.4067      71.0811      84.7130      100.1980
Precision for TP                           17223.5939    44183.7000     670.0129    6572.5926   100326.7087
Precision for SDD                           2999.2483     5616.9626     236.3724    1461.9453    15324.4121
Precision for GDD                            280.8333      142.0924      97.0395     250.8808      640.9328
Precision for SIDI                         53414.0994   482901.9053     381.5683    7267.4675   355146.9300
Precision for CNTY                            82.5343       20.1227      49.7289      80.2782      128.4888
Phi for CNTY                                   0.3776        0.1818       0.0824       0.3620        0.7537
Precision for AERCODE.id                   10306.3874    27528.9320     402.6150    3828.2774    60696.8187
Precision for AERCODE.id2                  28921.8609    16484.6946    8847.2268   25128.4367    71364.9426

[[2]]


[[3]]


Table: Model Diagnostic Metrics

      DIC        CPO         MSE          R2
---------  ---------  ----------  ----------
 -531.181   256.4403   0.0093379   0.6183205
[[1]]


[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.5981288 0.0155635 0.6698219 2 
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]


Table: Summary Table of Model Estimates

                                                 mean           sd   0.025quant     0.5quant   0.975quant
----------------------------------------  -----------  -----------  -----------  -----------  -----------
(Intercept)                                    3.9142       0.0242       3.8667       3.9142       3.9617
PERC_IRR                                       0.0109       0.0097      -0.0081       0.0109       0.0300
ACRES                                          0.0000       0.0000       0.0000       0.0000       0.0000
Precision for the Gaussian observations       86.2391       7.6658      71.8671      86.0186     101.9578
Precision for TP                           15131.8255   34172.5280     673.0431    6339.7215   83955.9023
Precision for SDD                           2510.4012    4095.0356     222.5436    1336.5574   12065.9013
Precision for GDD                            278.8893     140.9779      97.9555     248.7238     636.2787
Precision for RICH                         10356.9579   45322.9461     287.2311    2702.7558   66405.4899
Precision for CNTY                            82.8027      19.8757      50.2519      80.6254     128.0671
Phi for CNTY                                   0.3685       0.1822       0.0810       0.3483       0.7561
Precision for AERCODE.id                    9408.9618   24179.3118     398.3579    3606.0822   54288.2564
Precision for AERCODE.id2                  28976.3573   16190.7187    8814.5371   25380.3768   70402.0258

[[2]]


[[3]]


Table: Model Diagnostic Metrics

       DIC        CPO         MSE          R2
----------  ---------  ----------  ----------
 -533.7965   257.4915   0.0091007   0.6215751
[[1]]


[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.5209294 0.0153397 0.4925593 2 
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]


Table: Summary Table of Model Estimates

                                                mean          sd   0.025quant    0.5quant   0.975quant
----------------------------------------  ----------  ----------  -----------  ----------  -----------
(Intercept)                                   3.9848      0.0686       3.8529      3.9839       4.1223
PERC_IRR                                      0.0151      0.0026       0.0101      0.0151       0.0202
ACRES                                         0.0000      0.0000       0.0000      0.0000       0.0000
Precision for the Gaussian observations      33.2167      1.9800      29.4569     33.1711      37.2527
Precision for TP                            151.7446     96.4373      41.2759    127.8930     401.7231
Precision for SDD                           560.1093    680.2609      80.6495    356.1705    2284.8543
Precision for GDD                           266.7867    211.9980      56.6562    207.9632     828.8339
Precision for SDI                          2570.0639   8503.7065     115.8524    842.5804   15612.0570
Precision for CNTY                            8.4252      1.1653       6.3586      8.3486      10.9376
Phi for CNTY                                  0.2796      0.0779       0.1462      0.2733       0.4494
Precision for AERCODE.id                    101.1539     43.4153      42.4556     92.5641     209.6318
Precision for AERCODE.id2                  3230.8422   1315.6535    1416.8973   2980.2756    6493.6589

[[2]]


[[3]]


Table: Model Diagnostic Metrics

       DIC        CPO         MSE          R2
----------  ---------  ----------  ----------
 -345.7382   124.6112   0.0224348   0.8536887
[[1]]


[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.8779729 0.0385213 0.8328432 2 
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]


Table: Summary Table of Model Estimates

                                                mean          sd   0.025quant    0.5quant   0.975quant
----------------------------------------  ----------  ----------  -----------  ----------  -----------
(Intercept)                                   3.9028      0.0751       3.7561      3.9024       4.0513
PERC_IRR                                      0.0135      0.0024       0.0087      0.0135       0.0183
ACRES                                         0.0000      0.0000       0.0000      0.0000       0.0000
Precision for the Gaussian observations      34.0616      2.0674      30.1020     34.0313      38.2231
Precision for TP                            166.1384    111.7114      44.2473    137.0613     459.8257
Precision for SDD                           795.9452   1182.2189      95.2220    451.6111    3607.2307
Precision for GDD                           236.0298    169.8944      56.1832    190.9294     683.7322
Precision for SIDI                           64.6477     87.2374       7.3970     38.7327     280.1396
Precision for CNTY                            9.0385      1.2618       6.7871      8.9610      11.7467
Phi for CNTY                                  0.3260      0.0880       0.1725      0.3198       0.5144
Precision for AERCODE.id                     99.7972     41.6958      41.7493     92.1054     202.6530
Precision for AERCODE.id2                  3228.1650   1270.5250    1400.2791   3012.8177    6321.3133

[[2]]


[[3]]


Table: Model Diagnostic Metrics

       DIC        CPO         MSE          R2
----------  ---------  ----------  ----------
 -364.8695   134.1699   0.0218895   0.8566682
[[1]]


[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.9656443 0.0473564 0.7630333 2 
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]


Table: Summary Table of Model Estimates

                                                mean          sd   0.025quant    0.5quant   0.975quant
----------------------------------------  ----------  ----------  -----------  ----------  -----------
(Intercept)                                   4.0392      0.0687       3.9065      4.0384       4.1762
PERC_IRR                                      0.0134      0.0025       0.0084      0.0134       0.0184
ACRES                                         0.0000      0.0000       0.0000      0.0000       0.0000
Precision for the Gaussian observations      33.5108      2.0170      29.6989     33.4576      37.6385
Precision for TP                            175.7980    118.2568      44.5864    145.6604     483.8306
Precision for SDD                           799.4793   1218.8488      90.9053    446.3809    3687.8303
Precision for GDD                           218.1584    157.0026      52.0294    176.4296     631.5433
Precision for RICH                          194.8733    133.8561      48.4094    160.3397     543.9997
Precision for CNTY                            9.0170      1.2649       6.7656      8.9370      11.7374
Phi for CNTY                                  0.3151      0.0847       0.1656      0.3099       0.4946
Precision for AERCODE.id                    108.3525     46.5903      44.9076     99.3306     223.7402
Precision for AERCODE.id2                  3392.9496   1388.9797    1465.2164   3134.1177    6805.8228

[[2]]


[[3]]


Table: Model Diagnostic Metrics

       DIC        CPO         MSE          R2
----------  ---------  ----------  ----------
 -348.8429   125.4483   0.0220956   0.8541136
[[1]]


[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
R2 MSE NSE OutlierCount
0.7977482 0.0715224 0.7522307 2

Climate Effects Summary for Midwest

Climate Effects Summary for South

Climate Effects Summary for Northeast

Climate Effects Summary for West